Submit to Electronics Review for Electronics

Journal Menu

Journal Browser

► Journal Browser

Electronics, Volume 13, Issue 16 (August-2 2024) – 6 articles

Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
You may sign up for e-mail alerts to receive table of contents of newly released issues.
PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.

Order results

Result details

Section

Show export options Show export options

Select all

Export citation of selected articles as:

20 pages, 12203 KiB

Open AccessArticle

Variable-Frequency Control for Totem-Pole Bridgeless Power Factor Correction Converter to Achieve Zero-Voltage Switching Without Zero-Crossing Detection Circuits

by He Xi, Liting Li, Guo Xu, Mei Su and Zhiqiang Cai

Electronics 2024, 13(16), 3108; https://doi.org/10.3390/electronics13163108 (registering DOI) - 6 Aug 2024

Abstract

The totem-pole bridgeless power factor correction (PFC) converter, known for its advantages including simple topology, capability for zero-voltage switching (ZVS), and low common mode interference, presents an opportunity to enhance the efficiency and environmental friendliness of power systems. However, these converters have issues such as ZVS, requiring zero-crossing detection (ZCD) under circuits’ critical continuous mode (CRM) or additional auxiliary resonant circuits, resulting in increased circuit costs and control complexity. Therefore, this paper proposes a variable switching frequency digital control method to achieve ZVS under a wide operating range without ZCD circuits. At the same time, under the premise of ZVS, an interleaved parallel scheme is adopted to further minimize the current ripple and enhance the quality of the current waveform. Finally, an experimental 2 kW two-phase interleaved totem-pole bridgeless PFC converter prototype is designed to verify that the proposed method is correct and effective. The experimental prototype can reach an efficiency of 97.78%. Full article

► Show Figures

Figure 1

1 pages, 122 KiB

Open AccessCorrection

Correction: Yang et al. Maritime Electro-Optical Image Object Matching Based on Improved YOLOv9. Electronics 2024, 13, 2774

by Shiman Yang, Zheng Cao, Ningbo Liu, Yanli Sun and Zhongxun Wang

Electronics 2024, 13(16), 3107; https://doi.org/10.3390/electronics13163107 (registering DOI) - 6 Aug 2024

Abstract

In the original publication [...] Full article

21 pages, 12275 KiB

Open AccessArticle

Segmentation Point Simultaneous Localization and Mapping: A Stereo Vision Simultaneous Localization and Mapping Method for Unmanned Surface Vehicles in Nearshore Environments

by Xiujing Gao, Xinzhi Lin, Fanchao Lin and Hongwu Huang

Electronics 2024, 13(16), 3106; https://doi.org/10.3390/electronics13163106 (registering DOI) - 6 Aug 2024

Abstract

Unmanned surface vehicles (USVs) in nearshore areas are prone to environmental occlusion and electromagnetic interference, which can lead to the failure of traditional satellite-positioning methods. This paper utilizes a visual simultaneous localization and mapping (vSLAM) method to achieve USV positioning in nearshore environments. To address the issues of uneven feature distribution, erroneous depth information, and frequent viewpoint jitter in the visual data of USVs operating in nearshore environments, we propose a stereo vision SLAM system tailored for nearshore conditions: SP-SLAM (Segmentation Point-SLAM). This method is based on ORB-SLAM2 and incorporates a distance segmentation module, which filters feature points from different regions and adaptively adjusts the impact of outliers on iterative optimization, reducing the influence of erroneous depth information on motion scale estimation in open environments. Additionally, our method uses the Sum of Absolute Differences (SAD) for matching image blocks and quadric interpolation to obtain more accurate depth information, constructing a complete map. The experimental results on the USVInland dataset show that SP-SLAM solves the scaling constraint failure problem in nearshore environments and significantly improves the robustness of the stereo SLAM system in such environments. Full article

(This article belongs to the Section Electrical and Autonomous Vehicles)

► Show Figures

Figure 1

10 pages, 2710 KiB

Open AccessArticle

High-Efficiency 5G-Band Rectifier with Impedance Dispersion Compensation Network

by Yiyang Kong, Xue Bai, Leijun Xu and Jianfeng Chen

Electronics 2024, 13(16), 3105; https://doi.org/10.3390/electronics13163105 (registering DOI) - 6 Aug 2024

Abstract

This paper proposes a microwave rectifier designed for the popular 5G band, featuring impedance dispersion compensation and a cross-type impedance matching network. The rectifier has an ultra-high power conversion efficiency. The compensation network employs two parallel transmission lines to counteract the nonlinear shift of the diode input impedance caused by frequency variation. Additionally, the cross-over impedance matching network enhances matching and minimizes losses. After rigorous theoretical analysis and simulation, the rectifier is fabricated. Experimental results show significant conversion efficiency in the 5G band (across 4–6.5 GHz). At an input power of 12 dBm, the rectifier achieves more than 60% efficiency between 4.8 and 6.4 GHz and more than 70% between 5.2 and 6.2 GHz, with a peak efficiency of 78.1%. Moreover, the rectifier maintains more than 50% efficiency over a wide input power range of 5 to 14 dBm. Full article

(This article belongs to the Special Issue Micro Energy Harvesters: Modelling, Design, and Applications)

► Show Figures

Figure 1

19 pages, 12475 KiB

Open AccessArticle

Design of an Embedded Test Bench for Organic Photovoltaic Module Testing

by Alberto Dolara, Ana Cabrera-Tobar, Emanuele Ogliari, Sonia Leva and Louise Hanne

Electronics 2024, 13(16), 3104; https://doi.org/10.3390/electronics13163104 (registering DOI) - 6 Aug 2024

Abstract

In this article, a multipurpose embedded system for testing organic photovoltaic modules is presented. It is designed to include all the features for real-time monitoring, data acquisition, and power conversion based on a Ćuk converter, providing useful data for scientific investigation of the outdoor operation of organic photovoltaic modules. The embedded system allows both the scan of the I–V curve and the continuous operation of the organic photovoltaic module, such as at its maximum power. Voltage and current at the terminals of the organic photovoltaic module under test and up to four temperatures are continuously measured and stored on a Secure Digital card. The communication interface allows the embedded system to connect with other instruments, such as irradiance sensors, with digital serial output. The embedded system is designed both for laboratory and in-the-field use: it can be powered either by the AC electrical grid or a battery, which can also operate as a backup battery. Galvanic isolation divides the embedded system into the field-side and the logic-side functional sections, providing improved noise immunity and safe operation. The main power distribution system within the embedded system is a +9 V bus; ultra-low-noise linear low dropout regulators provide the +3.3 V and +5 V regulated voltages to supply the analog and digital circuits within the logic-side section, and a flyback converter supplies the field-side section of the board. The proposed embedded solution is validated using an experimental setup built at SolarTechLab, Politecnico di Milano. The experimental results report the feasibility of the proposed embedded system. Full article

(This article belongs to the Section Industrial Electronics)

► Show Figures

Figure 1

16 pages, 8230 KiB

Open AccessArticle

StrawSnake: A Real-Time Strawberry Instance Segmentation Network Based on the Contour Learning Approach

by Zhiyang Guo, Xing Hu, Baigan Zhao, Huaiwei Wang and Xueying Ma

Electronics 2024, 13(16), 3103; https://doi.org/10.3390/electronics13163103 (registering DOI) - 6 Aug 2024

Viewed by 66

Abstract

Automated harvesting systems rely heavily on precise and real-time fruit recognition, which is essential for improving efficiency and reducing labor costs. Strawberries, due to their delicate structure and complex growing environments, present unique challenges for automated recognition systems. Current methods predominantly utilize pixel-level and box-based approaches, which are insufficient for real-time applications due to their inability to accurately pinpoint strawberry locations. To address these limitations, this study proposes StrawSnake, a contour-based detection and segmentation network tailored for strawberries. By designing a strawberry-specific octagonal contour and employing deep snake convolution (DSConv) for boundary feature extraction, StrawSnake significantly enhances recognition accuracy and speed. The Multi-scale Feature Reinforcement Block (MFRB) further strengthens the model by focusing on crucial boundary features and aggregating multi-level contour information, which improves global context comprehension. The newly developed TongStraw_DB database and the public StrawDI_Db1 database, consisting of 1080 and 3100 high-resolution strawberry images with manually segmented ground truth contours, respectively, serves as a robust foundation for training and validation. The results indicate that StrawSnake achieves real-time recognition capabilities with high accuracy, outperforming existing methods in various comparative tests. Ablation studies confirm the effectiveness of the DSConv and MFRB modules in boosting performance. StrawSnake’s integration into automated harvesting systems marks a substantial step forward in the field, promising enhanced precision and efficiency in strawberry recognition tasks. This innovation underscores the method’s potential to transform automated harvesting technologies, making them more reliable and effective for practical applications. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Deep Learning and Its Applications)

► Show Figures